scholarly journals BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains

2018 ◽  
Author(s):  
Linxing Jiang ◽  
Andrea Stocco ◽  
Darby M. Losey ◽  
Justin A. Abernethy ◽  
Chantel S. Prat ◽  
...  
Keyword(s):  
Problem Solving ◽  
Magnetic Stimulation ◽  
Human Subjects ◽  
Occipital Cortex ◽  
Collaborative Problem Solving ◽  
Incorrect Decision ◽  
Brain Signals ◽  
Noisy Signals ◽  
Brain Interface ◽  
The Brain

ABSTRACTWe present BrainNet which, to our knowledge, is the first multi-human non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the Tetris task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

scholarly journals Efectos de una sesión de ejercicio físico sobre la actividad no lineal de las ondas cerebrales en estado basal (Effects of a physical exercise session on nonlinear activity of brainwaves at baseline)

Retos ◽  
2020 ◽  
pp. 180-187
Author(s):  
Fernando Maureira Cid ◽  
Elizabeth Flores Ferro ◽  
Hernan Díaz Muñoz ◽  
Helaine Barroso dos Reis ◽  
Carlos Rueff-Barroso ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Brain Activity ◽  
Occipital Cortex ◽  
Exercise Session ◽  
Temporal Region ◽  
Theta Waves ◽  
Eyes Closed ◽  
Before And After ◽  
Brain Interface ◽  
The Brain

Introducción: en las últimas décadas el electroencefalograma se ha utilizado para estudiar los efectos del ejercicio físico sobre la actividad eléctrica cerebral, incluyendo nuevos paradigmas con matemáticas no lineales y teoría del caos. Material y método: El objetivo de la presente investigación fue determinar los efectos de 30 minutos de ejercicio físico aeróbico sobre la actividad neurofisiológica cerebral durante un estado basal. La muestra estuvo constituida por 13 varones voluntarios (siete experimentales y seis controles). El registro de la actividad cerebral (electroencefalografía) se realizó a través de un dispositivo cerebro-interfaz Emotiv Epoc® mientras los estudiantes permanecían dos minutos sentados con los ojos cerrados. Los registros se realizaron antes y después de un trabajo aeróbico de 30 minutos de trote. Resultados: las ondas delta presentan variaciones similares de los índices de Hurst entre sujetos del grupo control y experimental en las cortezas prefrontales temporales y occipitales, situación similar que ocurre con las ondas theta. Las ondas alfa resultan ser las más estables con pocas modificaciones entre la primera y segunda medición. Las ondas beta presentan variaciones similares en la región prefrontal y occipital entre el grupo control y experimental, pero en la región temporal existe mayor número de modificaciones en los sujetos que realizaron ejercicio físico. Las ondas gamma presentan mayor variabilidad en los sujetos controles con respecto a los experimentales. Conclusiones: Los índices de Hurst de las ondas delta, theta, alfa., beta y gamma de la corteza prefrontal, temporal y occipital en estado basal aumentan y disminuyen, sin encontrarse un patrón característico tras la intervención con ejercicio físico.Abstract. Introduction: In recent decades the electroencephalogram has been used to study the effects of physical exercise on brain electrical activity, including new paradigms with nonlinear mathematics and chaos theory. Material and method: The aim of this research was to determine the effects of 30 minutes of aerobic physical exercise on brain neurophysiological activity during at basal state. The sample consisted of 13 male volunteers (seven experimental and six controls).The recording of brain activity (electroencephalography) was performed through the brain-interface device Emotiv Epoc® while the students sat with their eyes closed for two minutes. The logs were performed before and after a 30-minute aerobic exercise.Results: delta waves show similar variations of Hurst indices between control and experimental group subjects in temporal and occipital prefrontal cortex, a similar situation as with theta waves. Alpha waves turn out to be the most stable with few modifications between the first and second measurements.The beta waves show similar variations in the prefrontal and occipital regions between the control and experimental groups, but in the temporal region there are more modifications in the subjects who performed physical exercise. Gamma waves show greater variability in control subjects compared to experimental ones.Conclusions: The Hurst indices of delta, theta, alpha, beta and gamma waves of the prefrontal, temporal and occipital cortex at baseline increase and decrease, without finding a characteristic pattern after intervention with physical exercise.

scholarly journals Playing 20 Questions with the Mind: Collaborative Problem Solving by Humans Using a Brain-to-Brain Interface

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0137303 ◽  
Author(s):  
Andrea Stocco ◽  
Chantel S. Prat ◽  
Darby M. Losey ◽  
Jeneva A. Cronin ◽  
Joseph Wu ◽  
...  
Keyword(s):  
Problem Solving ◽  
Collaborative Problem Solving ◽  
Collaborative Problem ◽  
Brain Interface ◽  
The Mind

Creative Problem Solving in Public School Supervision

1989 ◽  
Vol 20 (3) ◽  
pp. 320-332 ◽  
Author(s):  
David A. Shapiro ◽  
Nelson Moses
Keyword(s):  
Public School ◽  
Problem Solving ◽  
School Environment ◽  
Professional Growth ◽  
Cognitive Learning ◽  
School Personnel ◽  
Collaborative Problem Solving ◽  
The Public ◽  
School Supervision ◽  
Team Functioning

This article presents a practical and collegial model of problem solving that is based upon the literature in supervision and cognitive learning theory. The model and the procedures it generates are applied directly to supervisory interactions in the public school environment. Specific principles of supervision and related recommendations for collaborative problem solving are discussed. Implications for public school supervision are addressed in terms of continued professional growth of both supervisees and supervisors, interdisciplinary team functioning, and renewal and retention of public school personnel.

Assessing Collaborative Behavior in Students

2016 ◽  
Vol 32 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Katarina Krkovic ◽  
Sascha Wüstenberg ◽  
Samuel Greiff
Keyword(s):  
Problem Solving ◽  
Collaborative Problem Solving ◽  
Seventh Graders ◽  
Computer Assisted ◽  
Personality Tests ◽  
Problem Solving Skills ◽  
Knowledge Application ◽  
Computer Agent ◽  
Collaborative Behavior ◽  
Confirmatory Factor

Abstract. Skilful collaborative problem-solving is becoming increasingly important in various life areas. However, researchers are still seeking ways to assess and foster this skill in individuals. In this study, we developed a computer-assisted assessment for collaborative behavior (COLBAS) following the experiment-based assessment of behavior approach (objective personality tests; Cattell, 1958 ). The instrument captures participants’ collaborative behavior in problem-solving tasks using the MicroDYN approach while participants work collaboratively with a computer-agent. COLBAS can thereby assess problem-solving and collaborative behavior expressed through communication acts. To investigate its validity, we administered COLBAS to 483 German seventh graders along with MicroDYN as a measure of individual problem-solving skills and questions regarding the motivation to collaborate. A latent confirmatory factor analysis suggested a five-dimensional construct with two problem-solving dimensions (knowledge acquisition and knowledge application) and three collaboration dimensions (questioning, asserting, and requesting). The results showed that extending MicroDYN to include collaborative aspects did not considerably change the measurement of problem-solving. Finally, students who were more motivated to collaborate interacted more with the computer-agent but also obtained worse problem-solving results.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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